7 citations · 8 across the 5 of their papers we have counts for
7 papers · 1 filter
Jamais Vu: Exposing the Generalization Gap in Supervised Semantic Correspondence
Octave Mariotti, Zhipeng Du, Yash Bhalgat +2
Semantic correspondence (SC) aims to establish semantically meaningful matches across different instances of an object category. We illustrate how recent supervised SC methods rema…
Spatially-Adaptive Hash Encodings For Neural Surface Reconstruction
Thomas Walker, Octave Mariotti, Amir Vaxman +1
Positional encodings are a common component of neural scene reconstruction methods, and provide a way to bias the learning of neural fields towards coarser or finer representations…
GeoGen: Geometry-Aware Generative Modeling via Signed Distance Functions
Salvatore Esposito, Qingshan Xu, Kacper Kania +6
We introduce a new generative approach for synthesizing 3D geometry and images from single-view collections. Most existing approaches predict volumetric density to render multi-vie…
Improving Semantic Correspondence with Viewpoint-Guided Spherical Maps
Octave Mariotti, Oisin Mac Aodha, Hakan Bilen
Recent progress in self-supervised representation learning has resulted in models that are capable of extracting image features that are not only effective at encoding image level,…
ViewNeRF: Unsupervised Viewpoint Estimation Using Category-Level Neural Radiance Fields
Octave Mariotti, Oisin Mac Aodha, Hakan Bilen
We introduce ViewNeRF, a Neural Radiance Field-based viewpoint estimation method that learns to predict category-level viewpoints directly from images during training. While NeRF i…
ViewNet: Unsupervised Viewpoint Estimation from Conditional Generation
Octave Mariotti, Oisin Mac Aodha, Hakan Bilen
Understanding the 3D world without supervision is currently a major challenge in computer vision as the annotations required to supervise deep networks for tasks in this domain are…